Welcome
Preface
Supplemental Material
Conventions
Acknowledgments
Introduction
What is Machine Learning?
Types of Machine Learning
Terminology
Tables
Variable Types
Predictive Models
Data Analysis Pipeline
Evaluating Predictive Models
Simple Classification Example
K-fold Cross-Validation Example
Simple Regression Example
Underfitting and Overfitting
Bias and Variance
Summary
Predicting Behavior with Classification Models
k-nearest Neighbors
Indoor Location with Wi-Fi Signals
Performance Metrics
Confusion Matrix
Decision Trees
Activity Recognition with Smartphones
Naive Bayes
Activity Recognition with Naive Bayes
Dynamic Time Warping
Hand Gesture Recognition
Dummy Models
Most-frequent-class Classifier
Uniform Classifier
Frequency-based Classifier
Other Dummy Classifiers
Summary
Predicting Behavior with Ensemble Learning
Bagging
Activity recognition with Bagging
Random Forest
Stacked Generalization
Multi-view Stacking for Home Tasks Recognition
Summary
Exploring and Visualizing Behavioral Data
Talking with Field Experts
Summary Statistics
Class Distributions
User-Class Sparsity Matrix
Boxplots
Correlation Plots
Interactive Correlation Plots
Timeseries
Interactive Timeseries
Multidimensional Scaling (MDS)
Heatmaps
Automated EDA
Summary
Preprocessing Behavioral Data
Missing Values
Imputation
Smoothing
Normalization
Imbalanced Classes
Random Oversampling
SMOTE
Information Injection
One-hot Encoding
Summary
Discovering Behaviors with Unsupervised Learning
K-means clustering
Grouping Student Responses
The Silhouette Index
Mining Association Rules
Finding Rules for Criminal Behavior
Summary
Encoding Behavioral Data
Feature Vectors
Timeseries
Transactions
Images
Recurrence Plots
Computing Recurence Plots
Recurrence Plots of Hand Gestures
Bag-of-Words
BoW for Complex Activities.
Graphs
Complex Activities as Graphs
Summary
Predicting Behavior with Deep Learning
Introduction to Artificial Neural Networks
Sigmoid and ReLU Units
Assembling Units into Layers
Deep Neural Networks
Learning the Parameters
Parameter Learning Example in R
Stochastic Gradient Descent
Keras and TensorFlow with R
Keras Example
Classification with Neural Networks
Classification of Electromyography Signals
Overfitting
Early Stopping
Dropout
Fine-Tuning a Neural Network
Convolutional Neural Networks
Convolutions
Pooling Operations
CNNs with Keras
Example 1
Example 2
Smiles Detection with a CNN
Summary
Multi-User Validation
Mixed Models
Skeleton Action Recognition with Mixed Models
User-Independent Models
User-Dependent Models
User-Adaptive Models
Transfer Learning
A User-Adaptive Model for Activity Recognition
Summary
Detecting Abnormal Behaviors
Isolation Forests
Detecting Abnormal Fish Behaviors
Explore and Visualize Trajectories
Preprocessing and Feature Extraction
Training the Model
ROC curve and AUC
Autoencoders
Autoencoders for Anomaly Detection
Summary
Setup Your Environment
Installing the Datasets
Installing the Examples Source Code
Running Shiny Apps
Installing Keras and TensorFlow
Datasets
COMPLEX ACTIVITIES
DEPRESJON
ELECTROMYOGRAPHY
FISH TRAJECTORIES
HAND GESTURES
HOME TASKS
HOMICIDE REPORTS
INDOOR LOCATION
SHEEP GOATS
SKELETON ACTIONS
SMARTPHONE ACTIVITIES
SMILES
STUDENTS' MENTAL HEALTH
Citing this Book